Data Challenge¶
The Idea¶
One of my hobbies is baking. I love to bake cakes, cookies, tarts, and all those delicious treats. This hobby sparked my idea for this challenge. We all know about the apple, butter, carrot, or milk in the back of the fridge that is about to spoil. You can still eat it, but it doesn't look appetizing, or it's too much to use in a short amount of time. For this, I thought it would be a good challenge to create an app that could identify the ingredients you upload with a picture and use that list of ingredients to find a recipe so you don't have to throw those ingredients away.
Approach¶
My approach to this challenge is to start with a CNN model that can identify the ingredients in a picture that you upload. From this model, an ingredient list will be made. This list will then be used in an NLP model to find a recipe that you could make to use those ingredients. For this, I spent some time finding images I can use to create this CNN model. I found a dataset that seems to be good enough for now at Roboflow.
After having found the data I started with loading the images and research how to best train a model to detect the ingredients in the dataset.
Loading the packages¶
Starting of with loading the packages that i am going to use in the notebook.
In [29]:
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import random
import pandas as pd
import tensorflow as tf
from collections import Counter
from tensorflow.keras import models, layers
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.vgg16 import preprocess_input
Loading the data¶
In [30]:
dataset_root = "FridgeDetection_data"
splits = ["train", "test", "valid"]
def load_data(split):
image_dir = os.path.join(dataset_root, split, "images")
label_dir = os.path.join(dataset_root, split, "labelTxt")
images, labels = [], []
for file in os.listdir(image_dir):
if file.lower().endswith((".jpg", ".png")):
img_path = os.path.join(image_dir, file)
label_path = os.path.join(label_dir, os.path.splitext(file)[0] + ".txt")
images.append(cv2.imread(img_path))
labels.append(open(label_path).read() if os.path.exists(label_path) else None)
return images, labels
data = {split: dict(zip(["images", "labels"], load_data(split))) for split in splits}
# Print summary
for split in splits:
print(f"{split.capitalize()}: Loaded {len(data[split]['images'])} images and {len(data[split]['labels'])} labels.")
Train: Loaded 1521 images and 1521 labels. Test: Loaded 73 images and 73 labels. Valid: Loaded 145 images and 145 labels.
data understanding¶
In [31]:
def plot_images_with_labels(images, labels, title, num_images=10, max_columns=2):
combined = list(zip(images, labels))
random.shuffle(combined)
images, labels = zip(*combined[:num_images])
num_rows = (len(images) + max_columns - 1) // max_columns
fig, axes = plt.subplots(num_rows, max_columns, figsize=(10, 5 * num_rows))
axes = axes.flatten() if num_images > 1 else [axes]
for ax, img, label_text in zip(axes, images, labels):
img = img.copy()
if label_text:
for line in label_text.strip().splitlines():
parts = line.split()
if len(parts) >= 9:
coords = list(map(int, parts[:8]))
label = parts[8]
pts = np.array(coords).reshape((4, 2))
cv2.polylines(img, [pts], isClosed=True, color=(0, 0, 255), thickness=2)
cv2.putText(img, label, tuple(pts[0]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax.axis("off")
# Hide unused subplots
for ax in axes[len(images):]:
ax.axis("off")
fig.suptitle(title, fontsize=16)
plt.subplots_adjust(top=0.92, hspace=0.3)
plt.show()
# Example usage
plot_images_with_labels(data['train']['images'], data['train']['labels'], "Train Set with Labels")
plot_images_with_labels(data['valid']['images'], data['valid']['labels'], "Valid Set with Labels")
In [32]:
# Function to count labels
def count_labels(data):
label_counts = Counter()
for split in data:
for label_data in data[split]['labels']:
if label_data:
for line in label_data.splitlines():
parts = line.split()
if len(parts) >= 9:
label = parts[8]
label_counts[label] += 1
return label_counts
# Count labels across all splits
label_counts = count_labels(data)
# Print the counts
for label, count in label_counts.items():
print(f"{label}: {count}")
crackers: 189 sausages: 359 sprite: 186 chocolate_drink: 363 coke: 218 orange: 222 apple: 169 paprika: 238 noodles: 191 cereal: 231 grape_juice: 254 basket: 92 orange_juice: 210 scrubby: 238 sponge_opl: 200 cloth_opl: 92 potato_chips: 191 pringles: 213 potato: 251 onion: 344 garlic: 224 butter: 152 eggs: 347 tomato: 199 lemon: 206 tray: 98 help_me_carry_opl: 37
In [33]:
# Check the size of the first image in the training set
image_shape = data['train']['images'][0].shape
print(f"Image size: {image_shape}")
Image size: (640, 640, 3)
Prepropecessing¶
In [34]:
# Ensure the input data is preprocessed
X_train_preprocessed = preprocess_input(np.array(data['train']['images']))
X_val_preprocessed = preprocess_input(np.array(data['valid']['images']))
y_train = data['train']['labels']
y_val = data['valid']['labels']
X_test_preprocessed = preprocess_input(np.array(data['test']['images']))
y_test = data['test']['labels']
In [35]:
def resize(img):
# Convert to RGB
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Normalize pixel values to [0, 1]
img = img.astype("float32") / 255.0
# Clip values to ensure they are within the valid range [0, 1] for floats
img = np.clip(img, 0.0, 1.0)
# Resize image
img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_CUBIC)
return img
# Apply preprocessing to all sets
X_train = np.array([resize(img) for img in X_train_preprocessed])
X_val = np.array([resize(img) for img in X_val_preprocessed])
X_test = np.array([resize(img) for img in X_test_preprocessed])
In [36]:
def resize_labels(labels, original_size, new_size):
"""
Resize label coordinates proportionally to the new image size.
Args:
labels (list): List of label strings.
original_size (tuple): Original image size (width, height).
new_size (tuple): New image size (width, height).
Returns:
list: Resized labels with integer coordinates.
"""
resized_labels = []
scale_x = new_size[0] / original_size[0]
scale_y = new_size[1] / original_size[1]
for label_data in labels:
if label_data:
resized_label_data = []
for line in label_data.splitlines():
parts = line.split()
if len(parts) >= 9:
x1, y1, x2, y2, x3, y3, x4, y4, label = parts[:9]
coords = [int(int(p) * scale_x if i % 2 == 0 else int(p) * scale_y) for i, p in enumerate([x1, y1, x2, y2, x3, y3, x4, y4])]
resized_label_data.append(f"{coords[0]} {coords[1]} {coords[2]} {coords[3]} {coords[4]} {coords[5]} {coords[6]} {coords[7]} {label}")
resized_labels.append("\n".join(resized_label_data))
else:
resized_labels.append(None)
return resized_labels# Example usage
original_size = (640, 640) # Assuming original images are 640x640
new_size = (224, 224) # Resized images are 224x224
# Resize labels for train, validation, and test sets
resized_train_labels = resize_labels(y_train, original_size, new_size)
resized_val_labels = resize_labels(y_val, original_size, new_size)
resized_test_labels = resize_labels(y_test, original_size, new_size)
# Print a few examples to verify
print("Original label:", y_train[0])
print("Resized label:", resized_train_labels[0])
Original label: 121 107 522 107 522 418 121 418 crackers 0 Resized label: 42 37 182 37 182 146 42 146 crackers
In [37]:
def plot_images_with_labels(images, labels, title, num_images=10, max_columns=2):
# Shuffle images and labels together
combined = list(zip(images, labels))
random.shuffle(combined)
images, labels = zip(*combined)
num_rows = (min(num_images, len(images)) + max_columns - 1) // max_columns
plt.figure(figsize=(10, 5 * num_rows))
for i in range(min(num_images, len(images))):
img = images[i].copy() # Make a copy of the image to avoid modifying the original image
if labels[i]:
for line in labels[i].splitlines():
parts = line.split()
if len(parts) >= 9:
# Extract the coordinates and label
x1, y1, x2, y2, x3, y3, x4, y4, label = parts[:9]
# Convert the coordinates to integers
x1, y1, x2, y2, x3, y3, x4, y4 = map(int, [x1, y1, x2, y2, x3, y3, x4, y4])
# Draw the label text
cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
# Draw the bounding box polygon
cv2.polylines(img, [np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]], np.int32)],
isClosed=True, color=(0, 0, 255), thickness=2)
# Plot the image
plt.subplot(num_rows, max_columns, i + 1)
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # Convert from BGR to RGB for displaying
plt.axis("off")
# Set title and show the plot
plt.suptitle(title)
plt.show()
# Plot 10 random images from the train set with labels
plot_images_with_labels(X_train, resized_train_labels, "Train Set with Labels")
# Plot 10 random images from the validation set with labels
plot_images_with_labels(X_val, resized_val_labels, "Valid Set with Labels")
y_train = resized_train_labels
y_val = resized_val_labels
y_test = resized_test_labels
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.04056507..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.042593174..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.06368705..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.05440599..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.06144396..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.030762283..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.03439017..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.054561775..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.040734805..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.019671302..255.0].
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.05351461..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.04559491..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.03984811..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.045102388..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.051864993..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.05315274..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.051676936..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.059529956..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.034241073..255.0]. Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.06011543..255.0].
In [38]:
# Preprocess labels into multi-hot encoded format
def encode_labels(labels, label_counts):
label_to_index = {label: idx for idx, label in enumerate(label_counts.keys())}
num_classes = len(label_counts)
encoded_labels = []
for label_data in labels:
multi_hot = np.zeros(num_classes, dtype=np.float32)
if label_data:
for line in label_data.splitlines():
parts = line.split()
if len(parts) >= 9:
label = parts[8]
if label in label_to_index:
multi_hot[label_to_index[label]] = 1.0
encoded_labels.append(multi_hot)
return np.array(encoded_labels)
# Apply preprocessing to train, validation, and test labels
y_train_encoded = encode_labels(y_train, label_counts)
y_val_encoded = encode_labels(y_val, label_counts)
y_test_encoded = encode_labels(y_test, label_counts)
Modeling¶
In [39]:
# Define the number of classes based on the label_counts
num_classes = len(label_counts)
model = models.Sequential([
layers.Input(shape=(224, 224, 3)),
layers.Conv2D(32, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(128, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes, activation='sigmoid') # sigmoid for multi-label
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
# Fit the model with preprocessed labels
history = model.fit(X_train, y_train_encoded, epochs=50, batch_size=32, validation_data=(X_val, y_val_encoded))
Epoch 1/50 48/48 [==============================] - 3s 53ms/step - loss: 0.3476 - accuracy: 0.0782 - val_loss: 0.2830 - val_accuracy: 0.0414 Epoch 2/50 48/48 [==============================] - 2s 51ms/step - loss: 0.2913 - accuracy: 0.0888 - val_loss: 0.2675 - val_accuracy: 0.1034 Epoch 3/50 48/48 [==============================] - 2s 51ms/step - loss: 0.2482 - accuracy: 0.1690 - val_loss: 0.2389 - val_accuracy: 0.1517 Epoch 4/50 48/48 [==============================] - 2s 51ms/step - loss: 0.1877 - accuracy: 0.3103 - val_loss: 0.2762 - val_accuracy: 0.1586 Epoch 5/50 48/48 [==============================] - 2s 52ms/step - loss: 0.1179 - accuracy: 0.4997 - val_loss: 0.3409 - val_accuracy: 0.1724 Epoch 6/50 48/48 [==============================] - 3s 52ms/step - loss: 0.0697 - accuracy: 0.5897 - val_loss: 0.4812 - val_accuracy: 0.1793 Epoch 7/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0352 - accuracy: 0.6391 - val_loss: 0.6073 - val_accuracy: 0.1655 Epoch 8/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0204 - accuracy: 0.6607 - val_loss: 0.7160 - val_accuracy: 0.2000 Epoch 9/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0147 - accuracy: 0.6693 - val_loss: 0.8110 - val_accuracy: 0.2207 Epoch 10/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0093 - accuracy: 0.6634 - val_loss: 0.8914 - val_accuracy: 0.1724 Epoch 11/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0073 - accuracy: 0.6746 - val_loss: 1.0178 - val_accuracy: 0.1793 Epoch 12/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0096 - accuracy: 0.6686 - val_loss: 0.9048 - val_accuracy: 0.1586 Epoch 13/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0106 - accuracy: 0.6818 - val_loss: 0.8884 - val_accuracy: 0.1655 Epoch 14/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0082 - accuracy: 0.6824 - val_loss: 1.1404 - val_accuracy: 0.1793 Epoch 15/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0070 - accuracy: 0.6693 - val_loss: 1.0610 - val_accuracy: 0.2000 Epoch 16/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0045 - accuracy: 0.6857 - val_loss: 1.0658 - val_accuracy: 0.1862 Epoch 17/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0050 - accuracy: 0.6719 - val_loss: 1.1569 - val_accuracy: 0.1793 Epoch 18/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0034 - accuracy: 0.6811 - val_loss: 1.0858 - val_accuracy: 0.1448 Epoch 19/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0047 - accuracy: 0.6686 - val_loss: 1.2278 - val_accuracy: 0.1793 Epoch 20/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0053 - accuracy: 0.6732 - val_loss: 1.2496 - val_accuracy: 0.1448 Epoch 21/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0029 - accuracy: 0.6785 - val_loss: 1.3164 - val_accuracy: 0.1586 Epoch 22/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0021 - accuracy: 0.6627 - val_loss: 1.2062 - val_accuracy: 0.1448 Epoch 23/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0017 - accuracy: 0.6772 - val_loss: 1.3522 - val_accuracy: 0.1724 Epoch 24/50 48/48 [==============================] - 3s 53ms/step - loss: 9.7288e-04 - accuracy: 0.6706 - val_loss: 1.4177 - val_accuracy: 0.1862 Epoch 25/50 48/48 [==============================] - 3s 53ms/step - loss: 7.8928e-04 - accuracy: 0.6805 - val_loss: 1.4408 - val_accuracy: 0.1793 Epoch 26/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0010 - accuracy: 0.6680 - val_loss: 1.5491 - val_accuracy: 0.1517 Epoch 27/50 48/48 [==============================] - 3s 53ms/step - loss: 7.8445e-04 - accuracy: 0.6634 - val_loss: 1.6396 - val_accuracy: 0.1517 Epoch 28/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0017 - accuracy: 0.6778 - val_loss: 1.4010 - val_accuracy: 0.1517 Epoch 29/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0019 - accuracy: 0.6686 - val_loss: 1.4283 - val_accuracy: 0.1517 Epoch 30/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0026 - accuracy: 0.6877 - val_loss: 1.5277 - val_accuracy: 0.1310 Epoch 31/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0034 - accuracy: 0.6844 - val_loss: 1.3715 - val_accuracy: 0.1517 Epoch 32/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0051 - accuracy: 0.6772 - val_loss: 1.3014 - val_accuracy: 0.1517 Epoch 33/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0102 - accuracy: 0.6877 - val_loss: 1.2557 - val_accuracy: 0.1517 Epoch 34/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0100 - accuracy: 0.6667 - val_loss: 1.0919 - val_accuracy: 0.1586 Epoch 35/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0083 - accuracy: 0.6713 - val_loss: 1.0321 - val_accuracy: 0.2000 Epoch 36/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0081 - accuracy: 0.6759 - val_loss: 1.1225 - val_accuracy: 0.1862 Epoch 37/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0050 - accuracy: 0.6693 - val_loss: 1.1026 - val_accuracy: 0.1724 Epoch 38/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0043 - accuracy: 0.6805 - val_loss: 1.1773 - val_accuracy: 0.2000 Epoch 39/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0027 - accuracy: 0.6798 - val_loss: 1.2086 - val_accuracy: 0.1862 Epoch 40/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0012 - accuracy: 0.6680 - val_loss: 1.3400 - val_accuracy: 0.1655 Epoch 41/50 48/48 [==============================] - 3s 53ms/step - loss: 0.0011 - accuracy: 0.6759 - val_loss: 1.4051 - val_accuracy: 0.1793 Epoch 42/50 48/48 [==============================] - 3s 53ms/step - loss: 6.5120e-04 - accuracy: 0.6844 - val_loss: 1.4013 - val_accuracy: 0.1793 Epoch 43/50 48/48 [==============================] - 3s 53ms/step - loss: 4.9846e-04 - accuracy: 0.6785 - val_loss: 1.4549 - val_accuracy: 0.1793 Epoch 44/50 48/48 [==============================] - 3s 53ms/step - loss: 4.5324e-04 - accuracy: 0.6805 - val_loss: 1.5013 - val_accuracy: 0.1724 Epoch 45/50 48/48 [==============================] - 3s 53ms/step - loss: 4.4196e-04 - accuracy: 0.6798 - val_loss: 1.5382 - val_accuracy: 0.1793 Epoch 46/50 48/48 [==============================] - 3s 53ms/step - loss: 4.0035e-04 - accuracy: 0.6798 - val_loss: 1.5708 - val_accuracy: 0.1793 Epoch 47/50 48/48 [==============================] - 3s 53ms/step - loss: 4.4776e-04 - accuracy: 0.6818 - val_loss: 1.5831 - val_accuracy: 0.1793 Epoch 48/50 48/48 [==============================] - 3s 54ms/step - loss: 4.5173e-04 - accuracy: 0.6798 - val_loss: 1.6299 - val_accuracy: 0.1655 Epoch 49/50 48/48 [==============================] - 3s 53ms/step - loss: 4.7867e-04 - accuracy: 0.6798 - val_loss: 1.6292 - val_accuracy: 0.1724 Epoch 50/50 48/48 [==============================] - 3s 53ms/step - loss: 3.6387e-04 - accuracy: 0.6824 - val_loss: 1.6656 - val_accuracy: 0.1724
In [40]:
# Plot accuracy and loss graphs
def plot_training_history(history):
# Extract accuracy and loss values
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
# Plot accuracy
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.plot(epochs, acc, 'b', label='Training Accuracy')
plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
# Plot loss
plt.subplot(1, 2, 2)
plt.plot(epochs, loss, 'b', label='Training Loss')
plt.plot(epochs, val_loss, 'r', label='Validation Loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.tight_layout()
plt.show()
# Call the function with the history object
plot_training_history(history)
In [41]:
base_model = MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights='imagenet')
base_model.trainable = False # Freeze weights
model = models.Sequential([
base_model,
layers.GlobalAveragePooling2D(),
layers.Dense(128, activation='relu'),
layers.Dropout(0.5),
# layers.Dense(64, activation='relu'),
# layers.Dropout(0.5),
# layers.Dense(32, activation='relu'),
# layers.Dropout(0.5),
layers.Dense(num_classes, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
# Fit the model with preprocessed labels
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True),
tf.keras.callbacks.ReduceLROnPlateau(factor=0.2, patience=5)
]
history = model.fit(X_train, y_train_encoded, epochs=50, batch_size=32, validation_data=(X_val, y_val_encoded), callbacks=callbacks)
# Plot accuracy and loss graphs
plot_training_history(history)
Epoch 1/50 48/48 [==============================] - 3s 43ms/step - loss: 0.3814 - accuracy: 0.0934 - val_loss: 0.2130 - val_accuracy: 0.3034 - lr: 0.0010 Epoch 2/50 48/48 [==============================] - 2s 35ms/step - loss: 0.2575 - accuracy: 0.1847 - val_loss: 0.1848 - val_accuracy: 0.3586 - lr: 0.0010 Epoch 3/50 48/48 [==============================] - 2s 35ms/step - loss: 0.2152 - accuracy: 0.3031 - val_loss: 0.1689 - val_accuracy: 0.4000 - lr: 0.0010 Epoch 4/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1879 - accuracy: 0.3596 - val_loss: 0.1524 - val_accuracy: 0.3448 - lr: 0.0010 Epoch 5/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1751 - accuracy: 0.3767 - val_loss: 0.1470 - val_accuracy: 0.4069 - lr: 0.0010 Epoch 6/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1593 - accuracy: 0.4398 - val_loss: 0.1417 - val_accuracy: 0.3655 - lr: 0.0010 Epoch 7/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1502 - accuracy: 0.4569 - val_loss: 0.1374 - val_accuracy: 0.4345 - lr: 0.0010 Epoch 8/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1414 - accuracy: 0.4694 - val_loss: 0.1378 - val_accuracy: 0.4414 - lr: 0.0010 Epoch 9/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1335 - accuracy: 0.4997 - val_loss: 0.1307 - val_accuracy: 0.4552 - lr: 0.0010 Epoch 10/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1272 - accuracy: 0.4957 - val_loss: 0.1324 - val_accuracy: 0.4345 - lr: 0.0010 Epoch 11/50 48/48 [==============================] - 2s 34ms/step - loss: 0.1204 - accuracy: 0.5378 - val_loss: 0.1328 - val_accuracy: 0.4345 - lr: 0.0010 Epoch 12/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1181 - accuracy: 0.5398 - val_loss: 0.1311 - val_accuracy: 0.4414 - lr: 0.0010 Epoch 13/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1149 - accuracy: 0.5378 - val_loss: 0.1249 - val_accuracy: 0.4345 - lr: 0.0010 Epoch 14/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1084 - accuracy: 0.5450 - val_loss: 0.1299 - val_accuracy: 0.4483 - lr: 0.0010 Epoch 15/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1068 - accuracy: 0.5700 - val_loss: 0.1229 - val_accuracy: 0.4690 - lr: 0.0010 Epoch 16/50 48/48 [==============================] - 2s 35ms/step - loss: 0.1020 - accuracy: 0.5819 - val_loss: 0.1243 - val_accuracy: 0.4759 - lr: 0.0010 Epoch 17/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0986 - accuracy: 0.5759 - val_loss: 0.1278 - val_accuracy: 0.4414 - lr: 0.0010 Epoch 18/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0988 - accuracy: 0.5733 - val_loss: 0.1210 - val_accuracy: 0.4966 - lr: 0.0010 Epoch 19/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0933 - accuracy: 0.5911 - val_loss: 0.1222 - val_accuracy: 0.4690 - lr: 0.0010 Epoch 20/50 48/48 [==============================] - 2s 34ms/step - loss: 0.0916 - accuracy: 0.6042 - val_loss: 0.1225 - val_accuracy: 0.4966 - lr: 0.0010 Epoch 21/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0908 - accuracy: 0.5937 - val_loss: 0.1209 - val_accuracy: 0.4690 - lr: 0.0010 Epoch 22/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0881 - accuracy: 0.6114 - val_loss: 0.1210 - val_accuracy: 0.4552 - lr: 0.0010 Epoch 23/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0876 - accuracy: 0.6121 - val_loss: 0.1250 - val_accuracy: 0.4966 - lr: 0.0010 Epoch 24/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0849 - accuracy: 0.6101 - val_loss: 0.1301 - val_accuracy: 0.4759 - lr: 0.0010 Epoch 25/50 48/48 [==============================] - 2s 34ms/step - loss: 0.0823 - accuracy: 0.6213 - val_loss: 0.1257 - val_accuracy: 0.4621 - lr: 0.0010 Epoch 26/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0813 - accuracy: 0.6252 - val_loss: 0.1244 - val_accuracy: 0.4897 - lr: 0.0010 Epoch 27/50 48/48 [==============================] - 2s 34ms/step - loss: 0.0775 - accuracy: 0.6345 - val_loss: 0.1246 - val_accuracy: 0.4828 - lr: 2.0000e-04 Epoch 28/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0735 - accuracy: 0.6213 - val_loss: 0.1265 - val_accuracy: 0.4621 - lr: 2.0000e-04 Epoch 29/50 48/48 [==============================] - 2s 34ms/step - loss: 0.0756 - accuracy: 0.6246 - val_loss: 0.1256 - val_accuracy: 0.4552 - lr: 2.0000e-04 Epoch 30/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0744 - accuracy: 0.6509 - val_loss: 0.1261 - val_accuracy: 0.4552 - lr: 2.0000e-04 Epoch 31/50 48/48 [==============================] - 2s 35ms/step - loss: 0.0733 - accuracy: 0.6489 - val_loss: 0.1257 - val_accuracy: 0.4621 - lr: 2.0000e-04
In [ ]: